Powering your application with deep learning is no walk in the park, but is certainly attainable with some tricks & good practice. Serving a deep learning model on a production system demands the model to be stable, reproducible, capable of isolation & to behave as a stand-alone package. One possible solution to this is a containerized microservice.

Ideally, serving deep learning microservices should be quick & efficient, without having to dive deep into the underlying algorithms & their implementation. Too good to be true? Not anymore! Together, we will demystify the process of developing, training, & deploying deep learning models as a web microservice using Model Asset Exchange, an open source framework developed at the IBM Center for Open Source Data & AI Technologies (CODAIT).

We will kick off with an overview of how deep learning models are best published as Docker Images on DockerHub, & are best prepared for deployment in local or cloud environments using Kubernetes or Docker. We highlight the following benefits of such an approach:

We will walk you through some super cool applications such as automatic image cropping, age estimation from videos/webcam & Veremin - a video theremin. All these applications & the framework itself are open source & we conclude by inviting contributions & opening the gates for you to be a part of this amazing initiative!

Saishruthi Swaminathan is a developer advocate & data scientist in the IBM CODAIT team whose main focus is to democratize data & AI through open source technologies. She has a Masters in Electrical Engineering specializing in Data Science & a Bachelor degree in Electronics & Instrumentation. Her passion is to dive deep into the ocean of data, extract insights & use AI for social good. Previously, she was working as a Software Developer. On a mission to spread the knowledge & experience, she acquired in her learning process. She also leads education for rural children initiative & organizing meetups focussing women empowerment.

Brought to you by Kaplan, Metis focuses primarily on Python, machine learning, data visualization, deep learning, big data processing, statistical foundations, & more. Students & alumni of the bootcamp program receive continuous support from our career advisors, empowering them to pursue a successful career in the fast-growing field of data science.

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